Cuyama Valley micronet

The hypothesis tested is that shrubs directly and indirectly buffer local changes in the microenvironment thereby functioning as refuges for other species within arid and semi-arid regions subject to dramatic global change drivers. To examine this hypothesis for Santa Barbara County, the following predictions will be tested: (i) shrub micro-environments reduce the level of stress and amplitude of variation associated with temperature and moisture, (ii) many plant and animal species including threatened lizards are relatively more common with shrubs within the region, and (iii) the variation in the interaction patterns between species relates to the extent of amelioration provided by shrub-biodiversity complexes within the region.

ecoblender

macro-climate analyses: 1980-2005

## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: (tmax^2)
## 
## Terms added sequentially (first to last)
## 
## 
##      Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                   149     378861              
## site  5    80972       144     297889 2.223e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $lsmeans
##  site    lsmean       SE df asymp.LCL asymp.UCL
##  site1 334.1493 9.096534 NA  316.3204  351.9781
##  site2 334.9719 9.096534 NA  317.1430  352.8008
##  site3 335.6182 9.096534 NA  317.7893  353.4470
##  site4 330.8118 9.096534 NA  312.9829  348.6407
##  site5 383.0701 9.096534 NA  365.2412  400.8990
##  site6 383.0701 9.096534 NA  365.2412  400.8990
## 
## Results are given on the identity (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast           estimate       SE df z.ratio p.value
##  site1 - site2 -8.226311e-01 12.86444 NA  -0.064  1.0000
##  site1 - site3 -1.468900e+00 12.86444 NA  -0.114  1.0000
##  site1 - site4  3.337465e+00 12.86444 NA   0.259  0.9998
##  site1 - site5 -4.892083e+01 12.86444 NA  -3.803  0.0020
##  site1 - site6 -4.892083e+01 12.86444 NA  -3.803  0.0020
##  site2 - site3 -6.462689e-01 12.86444 NA  -0.050  1.0000
##  site2 - site4  4.160096e+00 12.86444 NA   0.323  0.9995
##  site2 - site5 -4.809820e+01 12.86444 NA  -3.739  0.0025
##  site2 - site6 -4.809820e+01 12.86444 NA  -3.739  0.0025
##  site3 - site4  4.806365e+00 12.86444 NA   0.374  0.9991
##  site3 - site5 -4.745193e+01 12.86444 NA  -3.689  0.0031
##  site3 - site6 -4.745193e+01 12.86444 NA  -3.689  0.0031
##  site4 - site5 -5.225829e+01 12.86444 NA  -4.062  0.0007
##  site4 - site6 -5.225829e+01 12.86444 NA  -4.062  0.0007
##  site5 - site6  5.684342e-14 12.86444 NA   0.000  1.0000
## 
## P value adjustment: tukey method for comparing a family of 6 estimates
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: (prcp^2)
## 
## Terms added sequentially (first to last)
## 
## 
##      Df  Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                    149 2.3963e+12         
## site  5 9.939e+10       144 2.2969e+12   0.2844
## $lsmeans
##  site     lsmean       SE df asymp.LCL asymp.UCL
##  site1 135591.88 25259.44 NA  86084.29  185099.5
##  site2 129289.52 25259.44 NA  79781.93  178797.1
##  site3  86991.16 25259.44 NA  37483.57  136498.8
##  site4  83612.04 25259.44 NA  34104.45  133119.6
##  site5  72871.84 25259.44 NA  23364.25  122379.4
##  site6  72871.84 25259.44 NA  23364.25  122379.4
## 
## Results are given on the identity (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast           estimate       SE df z.ratio p.value
##  site1 - site2  6.302360e+03 35722.24 NA   0.176  1.0000
##  site1 - site3  4.860072e+04 35722.24 NA   1.361  0.7507
##  site1 - site4  5.197984e+04 35722.24 NA   1.455  0.6930
##  site1 - site5  6.272004e+04 35722.24 NA   1.756  0.4947
##  site1 - site6  6.272004e+04 35722.24 NA   1.756  0.4947
##  site2 - site3  4.229836e+04 35722.24 NA   1.184  0.8447
##  site2 - site4  4.567748e+04 35722.24 NA   1.279  0.7968
##  site2 - site5  5.641768e+04 35722.24 NA   1.579  0.6123
##  site2 - site6  5.641768e+04 35722.24 NA   1.579  0.6123
##  site3 - site4  3.379120e+03 35722.24 NA   0.095  1.0000
##  site3 - site5  1.411932e+04 35722.24 NA   0.395  0.9988
##  site3 - site6  1.411932e+04 35722.24 NA   0.395  0.9988
##  site4 - site5  1.074020e+04 35722.24 NA   0.301  0.9997
##  site4 - site6  1.074020e+04 35722.24 NA   0.301  0.9997
##  site5 - site6 -7.275958e-11 35722.24 NA   0.000  1.0000
## 
## P value adjustment: tukey method for comparing a family of 6 estimates

macro-climate analyses: 2005-2015

## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: (tmax^2)
## 
## Terms added sequentially (first to last)
## 
## 
##      Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                    65     398247         
## site  5    37507        60     360740   0.2837
## $lsmeans
##  site    lsmean       SE df asymp.LCL asymp.UCL
##  site1 336.3815 23.37896 NA  290.5596  382.2034
##  site2 336.9029 23.37896 NA  291.0810  382.7248
##  site3 338.5127 23.37896 NA  292.6908  384.3346
##  site4 333.4139 23.37896 NA  287.5920  379.2358
##  site5 386.7713 23.37896 NA  340.9494  432.5932
##  site6 386.7713 23.37896 NA  340.9494  432.5932
## 
## Results are given on the identity (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast           estimate       SE df z.ratio p.value
##  site1 - site2 -5.214182e-01 33.06284 NA  -0.016  1.0000
##  site1 - site3 -2.131210e+00 33.06284 NA  -0.064  1.0000
##  site1 - site4  2.967581e+00 33.06284 NA   0.090  1.0000
##  site1 - site5 -5.038978e+01 33.06284 NA  -1.524  0.6487
##  site1 - site6 -5.038978e+01 33.06284 NA  -1.524  0.6487
##  site2 - site3 -1.609792e+00 33.06284 NA  -0.049  1.0000
##  site2 - site4  3.488999e+00 33.06284 NA   0.106  1.0000
##  site2 - site5 -4.986837e+01 33.06284 NA  -1.508  0.6589
##  site2 - site6 -4.986837e+01 33.06284 NA  -1.508  0.6589
##  site3 - site4  5.098791e+00 33.06284 NA   0.154  1.0000
##  site3 - site5 -4.825857e+01 33.06284 NA  -1.460  0.6901
##  site3 - site6 -4.825857e+01 33.06284 NA  -1.460  0.6901
##  site4 - site5 -5.335737e+01 33.06284 NA  -1.614  0.5893
##  site4 - site6 -5.335737e+01 33.06284 NA  -1.614  0.5893
##  site5 - site6 -3.552714e-14 33.06284 NA   0.000  1.0000
## 
## P value adjustment: tukey method for comparing a family of 6 estimates
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: (prcp^2)
## 
## Terms added sequentially (first to last)
## 
## 
##      Df  Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                     65 6.8385e+11         
## site  5 9.022e+09        60 6.7483e+11   0.9769
## $lsmeans
##  site     lsmean       SE df asymp.LCL asymp.UCL
##  site1 106212.18 31976.08 NA  43540.21  168884.2
##  site2 103852.91 31976.08 NA  41180.94  166524.9
##  site3  79965.18 31976.08 NA  17293.21  142637.2
##  site4  76105.09 31976.08 NA  13433.12  138777.1
##  site5  83735.18 31976.08 NA  21063.21  146407.2
##  site6  83735.18 31976.08 NA  21063.21  146407.2
## 
## Results are given on the identity (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast           estimate       SE df z.ratio p.value
##  site1 - site2  2.359273e+03 45221.01 NA   0.052  1.0000
##  site1 - site3  2.624700e+04 45221.01 NA   0.580  0.9923
##  site1 - site4  3.010709e+04 45221.01 NA   0.666  0.9856
##  site1 - site5  2.247700e+04 45221.01 NA   0.497  0.9963
##  site1 - site6  2.247700e+04 45221.01 NA   0.497  0.9963
##  site2 - site3  2.388773e+04 45221.01 NA   0.528  0.9951
##  site2 - site4  2.774782e+04 45221.01 NA   0.614  0.9901
##  site2 - site5  2.011773e+04 45221.01 NA   0.445  0.9978
##  site2 - site6  2.011773e+04 45221.01 NA   0.445  0.9978
##  site3 - site4  3.860091e+03 45221.01 NA   0.085  1.0000
##  site3 - site5 -3.770000e+03 45221.01 NA  -0.083  1.0000
##  site3 - site6 -3.770000e+03 45221.01 NA  -0.083  1.0000
##  site4 - site5 -7.630091e+03 45221.01 NA  -0.169  1.0000
##  site4 - site6 -7.630091e+03 45221.01 NA  -0.169  1.0000
##  site5 - site6  3.637979e-12 45221.01 NA   0.000  1.0000
## 
## P value adjustment: tukey method for comparing a family of 6 estimates
##               prcp     srad swe       tmax       tmin         vp
## elevation 0.259915 0.437344  NA -0.9922931 -0.9905304 -0.8076309

## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: AI
## 
## Terms added sequentially (first to last)
## 
## 
##                   Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                                215     9011.4         
## climate.data$site  5   225.69       210     8785.7   0.3696

micro-climate analyses: 02/2016

## Linear mixed model fit by REML ['lmerMod']
## Formula: temp ~ microsite * site + (1 | month)
##    Data: hobo.data
## 
## REML criterion at convergence: 227074
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.9129 -0.7858 -0.3540  0.7276  3.3358 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  month    (Intercept)  15.87    3.984  
##  Residual             140.93   11.871  
## Number of obs: 29160, groups:  month, 4
## 
## Fixed effects:
##                     Estimate Std. Error t value
## (Intercept)         16.54637    1.99898   8.277
## micrositeshrub       0.41504    0.22807   1.820
## site                 0.27599    0.04841   5.701
## micrositeshrub:site  0.02210    0.06781   0.326
## 
## Correlation of Fixed Effects:
##             (Intr) mcrsts site  
## microstshrb -0.061              
## site        -0.066  0.581       
## mcrstshrb:s  0.048 -0.792 -0.713
##                        Estimate Std..Error   t.value  temp.pvalue
## (Intercept)         16.54636582 1.99897625 8.2774199 2.220446e-16
## micrositeshrub       0.41503738 0.22807048 1.8197769 6.879299e-02
## site                 0.27599492 0.04840759 5.7014808 1.187711e-08
## micrositeshrub:site  0.02210312 0.06781084 0.3259526 7.444603e-01
## Linear mixed model fit by REML ['lmerMod']
## Formula: smc ~ microsite * site + (1 | month)
##    Data: hobo.data
## 
## REML criterion at convergence: -58783.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.5605 -0.4880  0.0353  0.6009  3.1000 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  month    (Intercept) 0.002737 0.05232 
##  Residual             0.007780 0.08820 
## Number of obs: 29160, groups:  month, 4
## 
## Fixed effects:
##                       Estimate Std. Error t value
## (Intercept)          0.1347090  0.0261901   5.144
## micrositeshrub       0.0089624  0.0016945   5.289
## site                -0.0033538  0.0003597  -9.325
## micrositeshrub:site -0.0118808  0.0005038 -23.581
## 
## Correlation of Fixed Effects:
##             (Intr) mcrsts site  
## microstshrb -0.035              
## site        -0.038  0.581       
## mcrstshrb:s  0.027 -0.792 -0.713

shrub morphology

## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: shrub.size
## 
## Terms added sequentially (first to last)
## 
## 
##      Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                   179     2351.4              
## site  1   829.16       178     1522.3 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: z
## 
## Terms added sequentially (first to last)
## 
## 
##      Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                   179     43.096              
## site  1   4.6295       178     38.466 3.684e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

burrow patterns within Cuyama

## Analysis of Deviance Table
## 
## Model: Negative Binomial(22.9769), link: log
## 
## Response: burrows
## 
## Terms added sequentially (first to last)
## 
## 
##                           Df Deviance Resid. Df Resid. Dev Pr(>Chi)    
## NULL                                         35    154.853             
## microsite                  1  105.271        34     49.582   <2e-16 ***
## as.factor(site)            5    2.525        29     47.057   0.7728    
## microsite:as.factor(site)  5    4.624        24     42.433   0.4634    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $lsmeans
##  microsite site   lsmean        SE df asymp.LCL asymp.UCL
##  open         1 3.506558 0.1565481 NA  3.199729  3.813387
##  shrub        1 2.538974 0.2020473 NA  2.142968  2.934979
##  open         2 3.378725 0.1608445 NA  3.063475  3.693974
##  shrub        2 2.268684 0.2213370 NA  1.834871  2.702496
##  open         3 3.545779 0.1553148 NA  3.241367  3.850190
##  shrub        3 2.036882 0.2408019 NA  1.564919  2.508845
##  open         4 3.270836 0.1648197 NA  2.947795  3.593876
##  shrub        4 2.484907 0.2056334 NA  2.081873  2.887941
##  open         5 3.486355 0.1571986 NA  3.178252  3.794459
##  shrub        5 2.512306 0.2037998 NA  2.112865  2.911746
##  open         6 3.401197 0.1600576 NA  3.087490  3.714904
##  shrub        6 2.079442 0.2370105 NA  1.614909  2.543974
## 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast             estimate        SE df z.ratio p.value
##  open,1 - shrub,1   0.96758403 0.2555982 NA   3.786  0.0085
##  open,1 - open,2    0.12783337 0.2244510 NA   0.570  1.0000
##  open,1 - shrub,2   1.23787436 0.2711040 NA   4.566  0.0003
##  open,1 - open,3   -0.03922071 0.2205221 NA  -0.178  1.0000
##  open,1 - shrub,3   1.46967597 0.2872158 NA   5.117  <.0001
##  open,1 - open,4    0.23572233 0.2273166 NA   1.037  0.9969
##  open,1 - shrub,4   1.02165125 0.2584423 NA   3.953  0.0044
##  open,1 - open,5    0.02020271 0.2218529 NA   0.091  1.0000
##  open,1 - shrub,5   0.99425227 0.2569857 NA   3.869  0.0061
##  open,1 - open,6    0.10536052 0.2238878 NA   0.471  1.0000
##  open,1 - shrub,6   1.42711636 0.2840445 NA   5.024  <.0001
##  shrub,1 - open,2  -0.83975065 0.2582519 NA  -3.252  0.0526
##  shrub,1 - shrub,2  0.27029033 0.2996885 NA   0.902  0.9991
##  shrub,1 - open,3  -1.00680474 0.2548447 NA  -3.951  0.0045
##  shrub,1 - shrub,3  0.50209194 0.3143385 NA   1.597  0.9105
##  shrub,1 - open,4  -0.73186169 0.2607463 NA  -2.807  0.1769
##  shrub,1 - shrub,4  0.05406722 0.2882849 NA   0.188  1.0000
##  shrub,1 - open,5  -0.94738132 0.2559971 NA  -3.701  0.0116
##  shrub,1 - shrub,5  0.02666825 0.2869799 NA   0.093  1.0000
##  shrub,1 - open,6  -0.86222351 0.2577625 NA  -3.345  0.0393
##  shrub,1 - shrub,6  0.45953233 0.3114435 NA   1.475  0.9474
##  open,2 - shrub,2   1.11004098 0.2736074 NA   4.057  0.0029
##  open,2 - open,3   -0.16705408 0.2235926 NA  -0.747  0.9999
##  open,2 - shrub,3   1.34184260 0.2895799 NA   4.634  0.0002
##  open,2 - open,4    0.10788896 0.2302965 NA   0.468  1.0000
##  open,2 - shrub,4   0.89381788 0.2610671 NA   3.424  0.0304
##  open,2 - open,5   -0.10763066 0.2249052 NA  -0.479  1.0000
##  open,2 - shrub,5   0.86641890 0.2596253 NA   3.337  0.0403
##  open,2 - open,6   -0.02247286 0.2269127 NA  -0.099  1.0000
##  open,2 - shrub,6   1.29928298 0.2864349 NA   4.536  0.0004
##  shrub,2 - open,3  -1.27709507 0.2703937 NA  -4.723  0.0001
##  shrub,2 - shrub,3  0.23180161 0.3270713 NA   0.709  0.9999
##  shrub,2 - open,4  -1.00215202 0.2759631 NA  -3.631  0.0149
##  shrub,2 - shrub,4 -0.21622311 0.3021178 NA  -0.716  0.9999
##  shrub,2 - open,5  -1.21767165 0.2714801 NA  -4.485  0.0005
##  shrub,2 - shrub,5 -0.24362208 0.3008728 NA  -0.810  0.9997
##  shrub,2 - open,6  -1.13251384 0.2731456 NA  -4.146  0.0020
##  shrub,2 - shrub,6  0.18924200 0.3242901 NA   0.584  1.0000
##  open,3 - shrub,3   1.50889668 0.2865454 NA   5.266  <.0001
##  open,3 - open,4    0.27494305 0.2264691 NA   1.214  0.9880
##  open,3 - shrub,4   1.06087196 0.2576971 NA   4.117  0.0023
##  open,3 - open,5    0.05942342 0.2209844 NA   0.269  1.0000
##  open,3 - shrub,5   1.03347299 0.2562363 NA   4.033  0.0032
##  open,3 - open,6    0.14458123 0.2230272 NA   0.648  1.0000
##  open,3 - shrub,6   1.46633707 0.2833667 NA   5.175  <.0001
##  shrub,3 - open,4  -1.23395364 0.2918066 NA  -4.229  0.0014
##  shrub,3 - shrub,4 -0.44802472 0.3166554 NA  -1.415  0.9610
##  shrub,3 - open,5  -1.44947326 0.2875708 NA  -5.040  <.0001
##  shrub,3 - shrub,5 -0.47542370 0.3154678 NA  -1.507  0.9391
##  shrub,3 - open,6  -1.36431545 0.2891436 NA  -4.718  0.0002
##  shrub,3 - shrub,6 -0.04255961 0.3378751 NA  -0.126  1.0000
##  open,4 - shrub,4   0.78592891 0.2635349 NA   2.982  0.1136
##  open,4 - open,5   -0.21551963 0.2277651 NA  -0.946  0.9986
##  open,4 - shrub,5   0.75852994 0.2621066 NA   2.894  0.1428
##  open,4 - open,6   -0.13036182 0.2297476 NA  -0.567  1.0000
##  open,4 - shrub,6   1.19139402 0.2886859 NA   4.127  0.0022
##  shrub,4 - open,5  -1.00144854 0.2588368 NA  -3.869  0.0061
##  shrub,4 - shrub,5 -0.02739897 0.2895159 NA  -0.095  1.0000
##  shrub,4 - open,6  -0.91629073 0.2605830 NA  -3.516  0.0223
##  shrub,4 - shrub,6  0.40546511 0.3137819 NA   1.292  0.9803
##  open,5 - shrub,5   0.97404957 0.2573825 NA   3.784  0.0085
##  open,5 - open,6    0.08515781 0.2243431 NA   0.380  1.0000
##  open,5 - shrub,6   1.40691365 0.2844035 NA   4.947  <.0001
##  shrub,5 - open,6  -0.88889176 0.2591385 NA  -3.430  0.0298
##  shrub,5 - shrub,6  0.43286408 0.3125833 NA   1.385  0.9666
##  open,6 - shrub,6   1.32175584 0.2859937 NA   4.622  0.0002
## 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 12 estimates 
## Tests are performed on the log scale

vegetation patterns

## [1] all vegetation

## Analysis of Deviance Table
## 
## Model: poisson, link: log
## 
## Response: veg$richness
## 
## Terms added sequentially (first to last)
## 
## 
##                Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                             215     388.55         
## site            1  0.06240       214     388.48   0.8027
## microsite       1  0.90967       213     387.57   0.3402
## site:microsite  1  0.19238       212     387.38   0.6609

## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: veg$total.density
## 
## Terms added sequentially (first to last)
## 
## 
##                Df Deviance Resid. Df Resid. Dev Pr(>Chi)   
## NULL                             215     573210            
## site            1  12852.2       214     560357 0.023253 * 
## microsite       1  21860.8       213     538497 0.003081 **
## site:microsite  1   9384.4       212     529112 0.052490 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] native and exotic species processed independently
## Source: local data frame [6 x 4]
## Groups: site [3]
## 
##    site microsite   species         se
##   (int)    (fctr)     (dbl)      (dbl)
## 1     1      Open 0.5555556 0.04795503
## 2     1     Shrub 0.2222222 0.02910760
## 3     2      Open 0.7222222 0.06520270
## 4     2     Shrub 0.8333333 0.07471806
## 5     3      Open 1.0000000 0.07380124
## 6     3     Shrub 0.5000000 0.04207318

## Analysis of Deviance Table
## 
## Model: poisson, link: log
## 
## Response: veg$richness
## 
## Terms added sequentially (first to last)
## 
## 
##                Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                             215     388.55         
## site            1  0.06240       214     388.48   0.8027
## microsite       1  0.90967       213     387.57   0.3402
## site:microsite  1  0.19238       212     387.38   0.6609

## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: veg$native.density
## 
## Terms added sequentially (first to last)
## 
## 
##                Df Deviance Resid. Df Resid. Dev Pr(>Chi)  
## NULL                             215      77971           
## site            1  1456.77       214      76514  0.04184 *
## microsite       1   996.74       213      75518  0.09230 .
## site:microsite  1   950.91       212      74567  0.10013  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Source: local data frame [6 x 4]
## Groups: site [3]
## 
##    site microsite   species         se
##   (int)    (fctr)     (dbl)      (dbl)
## 1     1      Open 1.2222222 0.06415003
## 2     1     Shrub 0.8888889 0.06554983
## 3     2      Open 1.1111111 0.06125454
## 4     2     Shrub 1.1111111 0.06958048
## 5     3      Open 1.3888889 0.07431198
## 6     3     Shrub 1.4444444 0.08486251

## Analysis of Deviance Table
## 
## Model: poisson, link: log
## 
## Response: veg$exotic.richness
## 
## Terms added sequentially (first to last)
## 
## 
##                Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                             215     257.39         
## site            1  1.00827       214     256.38   0.3153
## microsite       1  0.01613       213     256.37   0.8989
## site:microsite  1  0.26333       212     256.10   0.6078

## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: veg$exotic.density
## 
## Terms added sequentially (first to last)
## 
## 
##                Df Deviance Resid. Df Resid. Dev Pr(>Chi)  
## NULL                             215     463225           
## site            1   5655.0       214     457570  0.09869 .
## microsite       1  13521.7       213     444049  0.01067 *
## site:microsite  1   4360.8       212     439688  0.14705  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: veg$prop.species.invaded
## 
## Terms added sequentially (first to last)
## 
## 
##                Df Deviance Resid. Df Resid. Dev Pr(>Chi)  
## NULL                             215     31.086           
## site            1  0.50481       214     30.581  0.06126 .
## microsite       1  0.02971       213     30.552  0.64978  
## site:microsite  1  0.00124       212     30.550  0.92614  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: veg$prop.density.invaded
## 
## Terms added sequentially (first to last)
## 
## 
##                Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                             215     41.680         
## site            1 0.086523       214     41.593   0.5065
## microsite       1 0.006609       213     41.587   0.8543
## site:microsite  1 0.020195       212     41.566   0.7483

shrub-annual interactions

##     site treatment total.density   richness
## 1      1   clipped  -1.000000000 -1.0000000
## 3      1 unclipped  -0.983333333 -0.5000000
## 5      1   clipped  -0.382198953 -0.1428571
## 7      1 unclipped  -0.975609756 -0.5000000
## 9      1   clipped  -1.000000000 -1.0000000
## 11     1 unclipped  -0.750000000 -0.1428571
## 13     2   clipped  -0.924528302  0.0000000
## 15     2 unclipped  -0.894736842  0.0000000
## 17     2   clipped  -0.600000000 -0.3333333
## 19     2 unclipped  -0.473684211 -0.3333333
## 21     2   clipped  -0.600000000 -0.3333333
## 23     2 unclipped  -0.084337349  0.2000000
## 25     3 unclipped  -0.263456091 -0.2000000
## 27     3   clipped   0.083056478 -0.1428571
## 29     3 unclipped   0.132075472  0.0000000
## 31     3   clipped  -0.223140496  0.0000000
## 33     3 unclipped  -0.583892617  0.0000000
## 35     3   clipped   0.022222222  0.1428571
## 37     4   clipped   0.469135802  0.3333333
## 39     4 unclipped  -0.495495495  0.1428571
## 41     4   clipped  -0.382716049  0.0000000
## 43     4 unclipped  -0.632000000  0.0000000
## 45     4   clipped  -0.057971014  0.0000000
## 47     4 unclipped   0.000000000  0.1428571
## 49     5 unclipped   0.510204082 -0.2000000
## 51     5   clipped  -0.887323944 -0.3333333
## 53     5 unclipped  -0.040816327  0.1428571
## 55     5   clipped   0.578947368 -0.6000000
## 57     5 unclipped  -0.808510638  0.3333333
## 59     5   clipped   0.234567901 -0.2000000
## 61     6 unclipped  -1.000000000 -1.0000000
## 63     6   clipped   0.120000000  0.0000000
## 65     6 unclipped  -0.125000000  0.0000000
## 67     6   clipped  -0.692307692  0.0000000
## 69     6 unclipped   0.185185185  0.0000000
## 71     6   clipped  -0.333333333  0.0000000
## 73     1   clipped   0.000000000  0.0000000
## 75     1 unclipped  -0.861111111 -0.2000000
## 77     1   clipped   0.000000000  0.0000000
## 79     1 unclipped   0.176470588  0.3333333
## 81     1   clipped   0.000000000  0.0000000
## 83     1 unclipped  -0.909090909 -0.3333333
## 85     2   clipped   0.000000000  0.0000000
## 87     2 unclipped  -0.643410853  0.2000000
## 89     2   clipped   1.000000000  1.0000000
## 91     2 unclipped  -0.188284519 -0.1428571
## 93     2   clipped   0.000000000  0.0000000
## 95     2 unclipped   0.142857143  0.2500000
## 97     3 unclipped  -0.490384615 -0.2000000
## 99     3   clipped   0.000000000  0.0000000
## 101    3 unclipped   0.580524345  0.0000000
## 103    3   clipped   0.000000000  0.0000000
## 105    3 unclipped  -0.333333333  0.1428571
## 107    3   clipped   0.000000000  0.0000000
## 109    4   clipped   0.000000000  0.0000000
## 111    4 unclipped  -0.395161290  0.2500000
## 113    4   clipped   0.000000000  0.0000000
## 115    4 unclipped  -0.047619048  0.2000000
## 117    4   clipped   0.000000000  0.0000000
## 119    4 unclipped   0.336405530 -0.2000000
## 121    5 unclipped   0.231527094  0.2000000
## 123    5   clipped   0.000000000  0.0000000
## 125    5 unclipped  -0.192052980  0.3333333
## 127    5   clipped   0.000000000  0.0000000
## 129    5 unclipped  -0.934065934 -0.5000000
## 131    5   clipped   1.000000000  1.0000000
## 133    6 unclipped  -1.000000000 -1.0000000
## 135    6   clipped   0.000000000  0.0000000
## 137    6 unclipped  -0.733333333 -0.5000000
## 139    6   clipped   0.000000000  0.0000000
## 141    6 unclipped   0.370078740  0.2000000
## 143    6   clipped   0.000000000  0.0000000
## 145    1   clipped   0.000000000  0.0000000
## 147    1 unclipped  -0.838709677 -0.2000000
## 149    1   clipped   0.000000000  0.0000000
## 151    1 unclipped  -0.568627451  0.0000000
## 153    1   clipped   0.000000000  0.0000000
## 155    1 unclipped  -0.534246575 -0.2000000
## 157    2   clipped   0.000000000  0.0000000
## 159    2 unclipped  -0.609756098 -0.2500000
## 161    2   clipped   0.000000000  0.0000000
## 163    2 unclipped   0.355704698  0.0000000
## 165    2   clipped   1.000000000  1.0000000
## 167    2 unclipped  -0.161290323  0.2500000
## 169    3 unclipped  -1.000000000 -1.0000000
## 171    3   clipped   1.000000000  1.0000000
## 173    3 unclipped   0.007092199 -0.4285714
## 175    3   clipped   0.000000000  0.0000000
## 177    3 unclipped  -0.439024390  0.0000000
## 179    3   clipped   0.000000000  0.0000000
## 181    4   clipped   0.000000000  0.0000000
## 183    4 unclipped  -0.057471264  0.0000000
## 185    4   clipped   0.000000000  0.0000000
## 187    4 unclipped   0.031578947  0.3333333
## 189    4   clipped   0.000000000  0.0000000
## 191    4 unclipped  -0.245614035  0.0000000
## 193    5 unclipped   0.120879121 -0.4285714
## 195    5   clipped   0.000000000  0.0000000
## 197    5 unclipped   0.229508197  0.0000000
## 199    5   clipped   0.000000000  0.0000000
## 201    5 unclipped  -0.637583893  0.2500000
## 203    5   clipped   0.000000000  0.0000000
## 205    6 unclipped  -0.941176471 -0.6000000
## 207    6   clipped   0.000000000  0.0000000
## 209    6 unclipped  -0.459459459  0.2000000
## 211    6   clipped   0.000000000  0.0000000
## 213    6 unclipped   0.066666667 -0.1428571
## 215    6   clipped   0.000000000  0.0000000

## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: rii.density
## 
## Terms added sequentially (first to last)
## 
## 
##      Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                   107     23.322         
## site  1  0.56623       106     22.756   0.1044
## 
##  One Sample t-test
## 
## data:  rii.veg$rii.density
## t = -4.0048, df = 107, p-value = 0.0001148
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.26896897 -0.09085533
## sample estimates:
##  mean of x 
## -0.1799122
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## -1.0000 -0.5428  0.0000 -0.1799  0.0000  1.0000

## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: rii.richness
## 
## Terms added sequentially (first to last)
## 
## 
##      Df  Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                    107     13.070         
## site  1 0.0052575       106     13.065   0.8364
## 
##  One Sample t-test
## 
## data:  rii.veg$rii.richness
## t = -1.296, df = 107, p-value = 0.1978
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.11025300  0.02308369
## sample estimates:
##   mean of x 
## -0.04358466
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -1.00000 -0.15710  0.00000 -0.04358  0.00000  1.00000

advanced climate-shrub-community interaction analyses

## 
## Call:
## glm(formula = CVt ~ microsite * site, family = gaussian(), data = growing.season.means)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.25148  -0.03921   0.01307   0.06146   0.11890  
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          0.80105    0.04576  17.506  2.9e-16 ***
## micrositeshrub      -0.02804    0.06473  -0.433 0.668360    
## site                -0.04288    0.01127  -3.807 0.000737 ***
## micrositeshrub:site  0.01775    0.01608   1.104 0.279380    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.008629518)
## 
##     Null deviance: 0.40985  on 30  degrees of freedom
## Residual deviance: 0.23300  on 27  degrees of freedom
## AIC: -53.638
## 
## Number of Fisher Scoring iterations: 2
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: CVt
## 
## Terms added sequentially (first to last)
## 
## 
##                Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                              30    0.40985              
## microsite       1 0.010366        29    0.39949    0.2731    
## site            1 0.155974        28    0.24351 2.124e-05 ***
## microsite:site  1 0.010516        27    0.23300    0.2696    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = CVs ~ microsite * site, family = gaussian(), data = growing.season.means)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -6.6107  -1.3671  -0.2915   1.4558   6.7215  
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)   
## (Intercept)           3.7787     1.2577   3.004  0.00568 **
## micrositeshrub        0.7361     1.7791   0.414  0.68231   
## site                 -0.3942     0.3096  -1.273  0.21381   
## micrositeshrub:site  -0.2432     0.4419  -0.550  0.58653   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 6.51897)
## 
##     Null deviance: 213.25  on 30  degrees of freedom
## Residual deviance: 176.01  on 27  degrees of freedom
## AIC: 151.81
## 
## Number of Fisher Scoring iterations: 2
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: CVs
## 
## Terms added sequentially (first to last)
## 
## 
##                Df Deviance Resid. Df Resid. Dev Pr(>Chi)  
## NULL                              30     213.25           
## microsite       1    0.020        29     213.23  0.95531  
## site            1   35.246        28     177.99  0.02006 *
## microsite:site  1    1.975        27     176.01  0.58200  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## lm(formula = temp ~ volume, data = hobo.means)
## 
## Residuals:
##       1       2       3       4       5       6 
## -1.5495  1.5373  0.5688  0.7040 -0.3850 -0.8756 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 17.19082    0.84805  20.271  3.5e-05 ***
## volume      -0.02663    0.08979  -0.297    0.782    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.275 on 4 degrees of freedom
## Multiple R-squared:  0.02151,    Adjusted R-squared:  -0.2231 
## F-statistic: 0.08793 on 1 and 4 DF,  p-value: 0.7816
## 
## Call:
## lm(formula = smc ~ volume, data = hobo.means)
## 
## Residuals:
##         1         2         3         4         5         6 
##  0.117825 -0.066861 -0.029697 -0.008686 -0.001715 -0.010866 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 0.103286   0.046374   2.227   0.0899 .
## volume      0.001391   0.004910   0.283   0.7910  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0697 on 4 degrees of freedom
## Multiple R-squared:  0.01966,    Adjusted R-squared:  -0.2254 
## F-statistic: 0.08024 on 1 and 4 DF,  p-value: 0.791